Chapter 3: Pharmacodynamics or What the Drug Does to the Body

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Hello and welcome back to the Deep Dive.

Hello.

Today we are doing something a little different, something we're calling a Last Minute Lecture.

We know a lot of you listening might be students, prepping for a massive exam or maybe you're a professional, realizing that the foundations of your pharmacology knowledge have gotten a little shaky over the years.

It happens to everyone.

We are going to tighten those bolts today.

We are taking a microscope to chapter three of Brenner and Stevens Pharmacology, sixth edition.

It is a foundational text.

The chapter title is Pharmacodynamics or What the Drug Does to the Body.

And really that title sums up the entire mission of the next hour.

What the drug does to the body.

It sounds so simple, but I think most people, they just stop at the surface level.

They think, I take an aspirin, my headache goes away.

But we are not stopping there.

We are looking at the mechanics,

the molecular gears and levers that actually make that happen.

Exactly.

We need to move past that magic black box understanding of medicine.

We aren't just memorizing that, you know, drug A treats disease B.

We are going to trace the causal chain.

We're going to look at what happens from the exact

millisecond a drug molecule binds to a receptor through all the chaos inside the cell to the final physiological effect.

And the idea is if you understand the mechanism, you don't have to memorize as much.

The clinical effects just start to make logical sense.

Okay.

So before we get into the heavy machinery, let's orient ourselves.

We've covered pharmacokinetics in previous discussions.

I always find it helpful to draw a hard line in the sand between that and what we're doing today.

How do you distinguish them?

It's the classic separation of church and state in pharmacology.

So pharmacokinetics or PK is what the body does to the drug.

That's all the logistics, absorption, distribution, metabolism, excretion.

It's how the package gets delivered to the site of action.

The journey.

The journey.

Pharmacodynamics or PD, which is our focus today, is what the drug does to the body.

It's what happens when the package is opened.

It describes the biological effects and the mechanisms that cause them.

So PK is the delivery truck and PD is the explosion, or I guess the healing that happens upon delivery.

That's a really good way to visualize it.

Yes.

Okay.

So let's look at our blueprint for this session.

We're going to be rigorous and follow the chapter structure exactly.

So if you have the book open, we're walking right through it.

We'll start with the nature of drug receptors, like physically, what are they?

Then we move to how we classify them and the math of binding.

And yes, we have to do the math.

We'll make the math intuitive.

Don't worry.

I mean, it's essential for understanding why some drugs work better than others.

After the math, we get into the heavy lifting

signal transduction, the molecular telephone game, as the outline calls it.

Then we'll look at efficacy, how receptors regulate themselves because they aren't static.

And finally, we will break down those infamous dose response curves that show up on every single exam.

Oh, yes.

The curves are where the theory needs reality.

You really can't practice medicine safely without understanding them.

All right.

Let's start with section one, the nature of drug receptors.

The text says that most drugs work by interacting with specific cell molecules called receptors.

When I first learned this, I had this cartoon image in my head of a, you know, a baseball mitt just waiting to catch a ball.

Right.

Is that accurate or are we dealing with something more complex?

It's a useful starting point, but it's a bit too passive.

Receptors are dynamic machines.

Chemically, we are almost always talking about proteins.

Proteins.

And these are massive, complex, three dimensional structures compared to the tiny drug molecules that bind to them.

They aren't just waiting to catch something.

They're waiting to be changed by something.

The binding causes a change in their shape.

A conformational change.

Exactly.

That's the signal.

Now, the text does mention that not everything targets a protein receptor though.

There are some exceptions.

True.

There are a few outliers.

Some antimicrobial and antineoplastic drugs, cancer drugs, they can target DNA directly.

They just bind right to the helix.

Right to the DNA.

And agents like general anesthetics or alcohol might work by dissolving into the membrane lipids and altering the cell that way.

Essentially, they change the fluidity of the wall.

But for the vast, vast majority of therapeutic drugs, and certainly for the focus of this chapter, we are targeting a protein designed to receive a signal.

And these proteins aren't all built the same.

The text breaks them down into four major families in table 3 .1.

I want to walk through these because they really dictate how fast a drug works and what it actually does.

The first family, and arguably the most important one for general pharmacology, is the G protein coupled receptors, or GPCRs.

The heavy hitters.

This is the largest family of receptors in the mammalian genome.

A huge number of our drugs target these.

If you look at figure 3 .1 in the text, you see this serpentine structure.

It looks like a snake.

It does.

It's a single polypeptide chain that threads back and forth through the cell membrane seven times.

Why seven?

Is that number significant?

Structurally, yes.

It's a very stable arrangement.

Those seven transmembrane domains create a specific 3D pocket, almost like a barrel or a bundle of logs.

The N terminal of the protein is outside the cell, wading in the extracellular fluid to catch the drug.

The catcher's mitt part.

The catcher's mitt part, exactly.

The C terminal is inside the cell.

When a drug hits the outside loops,

it physically shifts those seven helices, which changes the shape of the loop on the inside, specifically the third cytoplasmic loop.

That internal change is what rings the bell for the G protein.

It's a mechanical linkage.

You pull a lever on the outside, gears shift in the wall, and a button gets pressed on the inside.

Precisely.

Because it involves this mechanical shifting and then recruiting other proteins, it's not instantaneous.

We're talking seconds to minutes, but it's not the fastest system we have.

Okay, so let's contrast that with the second family.

Enzymes.

We usually think of enzymes as the workers causing chemical reactions, but drugs can target them too.

Right.

Enzymes are biological catalysts.

Usually in pharmacology, we're trying to inhibit them to stop a specific reaction from happening.

And the text makes a really critical distinction here between competitive inhibitors and non -competitive inhibitors.

This is a concept that applies broadly, not just to enzymes, but it's easiest to understand here.

Absolutely.

This is a huge concept.

So let's drill down on that.

Competitive implies a fight for the same real estate.

It is.

A competitive inhibitor is a drug molecule that looks enough like the natural substrate, the thing the enzyme is meant to process, that it can slide into the active site.

The parking spot.

The parking spot.

Yeah.

But it doesn't do anything.

It just sits there, blocking the spot.

However, because it's a competition, if you flood the system with enough of the natural substrate, you can eventually kick the drug out.

It's a numbers game.

So it's surmountable.

If I add more substrate, I win.

Exactly.

Now compare that to a non -competitive inhibitor.

These are sneakier.

They bind to a completely different spot on the enzyme, an L -asteric site.

Not the active site.

Not the active site at all.

But binding there causes the enzyme to twist or change shape.

So the active site doesn't work anymore.

Right.

The mouth of the enzyme might close up, or the internal catalytic machinery breaks alignment.

At that point, it doesn't matter how much substrate you have waiting to be processed.

The machine is broken.

You cannot overcome non -competitive inhibition just by adding more substrate.

And that's a key clinical difference.

Family number three.

Membrane transport proteins.

This seems to cover a few subcategories.

It does.

Broadly, we are talking about moving things across the cell wall.

First, you have legion gated ion channels.

Okay.

These are the speed demons of the nervous system.

A drug binds and a gate, a physical pore, swings open immediately.

How fast are we talking?

Milliseconds.

This is how your brain processes information in real time.

It has to be that fast.

The text also mentions voltage gated channels under this heading.

Right.

These are interesting because they don't wait for a chemical key.

They wait for an electrical change, a change in the membrane potential.

But drugs can still jam them.

How does that work?

Lidocaine is the classic example here.

It finds a specific site on the voltage gated sodium channel and locks it shut.

So if sodium can't rush in, the nerve can't fire.

That's how you get numbness.

Then under the same heading, you have the neurotransmitter transporters.

These aren't just letting things flow through passively.

They're actively grabbing things.

Right.

These are large proteins, often with 12 transmembrane domains,

that sit on the nerve terminal.

Their job is to grab neurotransmitters like serotonin or norepinephrine out of the synapse and drag them back inside the neuron.

To be recycled.

To be reused, exactly.

So if a drug blocks this transporter, it's a reuptake inhibitor.

You're plugging the drain.

And the sink fills up.

The neurotransmitter stays in the synapse longer, bouncing around and activating the next neuron repeatedly.

This is the mechanism for many antidepressants, like SSRIs.

Okay, which brings us to the fourth and final family,

the nuclear receptors.

The introverts of the receptor world.

These aren't on the cell surface at all.

They're intracellular proteins, dissolved in the cytoplasm or sitting inside the nucleus.

Which means the drug has to get inside the cell first.

Yes.

For a drug to reach them, it has to be lipid soluble enough to just slide right through the cell membrane on its own.

And once the drug finds the receptor inside, what happens?

They form a complex that migrates to the DNA.

They act as transcription factors.

They literally tell the DNA to start or stop making specific proteins.

And this explains the time lag we see with these drugs, doesn't it?

Exactly.

You can't make a new protein in milliseconds.

It takes time to transcribe the RNA, translate it, and fold the protein.

This is why if you give a patient a steroid for inflammation, you don't see the full effect for hours or even days.

You're re -engineering the cell's production line, not just flipping a switch.

So we have these four families, GPCRs, enzymes, transporters, and nuclear receptors.

But within those families, we have thousands of specific receptors.

Section two of the chapter talks about classification.

How do we keep them all organized without losing our minds?

It was a messy process historically.

We categorize them by what binds to them, where they are found in the body, and now what their amino acid sequence looks like.

The adrenoceptors.

The receptors for adrenaline are the best example of this evolution.

Walk us through that history a bit.

So early researchers knew adrenaline did different things in different tissues.

Sometimes it constricted blood vessels.

Sometimes it relaxed airways.

That seems contradictory.

It did.

It seemed contradictory if there was only one type of receptor.

So a researcher named Alquist proposed two types,

alpha,

alpha, and beta.

And beta.

A simple binary system to explain the different effects.

Right.

But then pharmacology advanced.

We found drugs that could block the heart effects of beta receptors, but didn't stop the airway relaxation.

So the beta bucket was too broad.

It had to be split.

It had to be split into beta one, which is mostly heart, and beta two, which is mostly lungs.

As our drugs got more specific, the classification had to get more granular.

Now we have alpha one, alpha two, and subtypes of those.

It's a hierarchy based on specificity.

And now with the human genome mapped, the text mentions orphan receptors.

That sounds a little dramatic.

What is that?

It's actually one of the most exciting frontiers in pharmacology.

So when we scan the human genome, we found all these DNA sequences that code for proteins that look exactly like receptors.

How so?

They have seven transmembrane domains, the binding pockets, everything.

We know they're receptors.

But we have no idea what natural chemical in the body is supposed to bind to them.

They are locks for which we haven't found the key.

Wow.

So why is that a goldmine, as the text puts it?

Because if you can figure out what that receptor does, maybe controls appetite or pain or memory, and you can design a drug to hit it, you have a brand new therapeutic class.

You can treat diseases we currently struggle with.

Exactly.

Because we'd be accessing a control system we didn't even know existed.

It's like the dark matter of the body.

That's fascinating.

Okay, let's move to section three, drug receptor interactions.

This is where we have to talk about the physical bond itself.

Right.

The drug has to stick.

And the text emphasizes that for most useful drugs, this bond is relatively weak.

We're talking hydrogen bonds, ionic bonds, or hydrophobic forces.

Why is weak good?

My intuition says you'd want a strong grip to make sure it works.

Well, you want reversibility.

You want the drug to bind, deliver the message, and then let go as the concentration in the blood drops.

So the body can return to normal.

Right.

If the bond is too strong, like a covalent bond, it's irreversible.

The receptor is effectively dead until the cell makes a new one.

Are there drugs that do that?

A few.

The text mentions some antineoplastic agents and cholinesterase inhibitors.

If they form a covalent bond, the cell has to synthesize entirely new receptors to recover function.

And that takes time.

Now, we have to deal with stereospecificity.

This is the lock and key concept again, isn't it?

It is.

Drugs are 3D objects.

Many have what's called a chiral center, meaning they can exist as left -handed S and right -handed R mirror images.

We call them enantiomers.

And the receptor is also a complex 3D shape.

Made of amino acids, yeah.

And usually only one of those hands fits the glove of the receptor.

So if you synthesize a drug mixture, half of it might be useless.

Or worse.

The useless half might bind to something else entirely and cause toxicity.

That's why modern drug manufacturing tries to purify just the active enantiomer.

Okay, take a deep breath.

We're doing the math.

Affinity.

This is a word we use a lot, but what is the mathematical definition?

Affinity is simply the measure of how tightly a drug binds to its receptor.

It's governed by the law of mass action.

You have an association rate.

Okay, one out of all.

That's how fast it clicks in.

And a dissociation rate, how fast it falls off.

And the ratio gives us the KD dollar, the dissociation constant.

I remember this being a huge tripping point in school.

It trips everyone up because it feels backward.

But here is the definition you need to burn into your brawn.

KD dollar is the concentration of drug required to occupy 50 % of the receptors.

Okay, concentration for 50 % occupancy.

Now think about the logic.

If a drug is super sticky, it has high affinity.

Do you need a lot of it or a little of it to fill half the seats?

Well, if it's sticky, it grabs on easily.

So I'd only need a little bit, a small amount.

Correct.

So a low concentration, a low number for KD dollar means high affinity.

This inverse relationship is the most common mistake students make.

Low number equals high stickiness.

High number equals low stickiness.

You've got it.

If the KD dollars is high, the drug is bouncing off constantly.

So you have to flood the tissue with a massive concentration just to keep the receptors occupied.

That clears it up.

Now that we've successfully bound the drug to the receptor, we move to section four, signal transduction.

What happens next?

The cascade.

And we're going to focus heavily on GPCRs here because they're the most complex and offer the most targets for drugs.

Figure 3 .3 in the text breaks this down into a specific cycle.

Walk us through the anatomy of this event.

Who are the players in the field?

You have three main components.

First, the receptor itself in the membrane.

Second, the G protein, which is kind of attached to the inside face of the membrane.

And third, the effector, which is usually an enzyme nearby waiting for instructions.

And the G protein is described as heterotrimeric.

That's just fancy speak for it.

It has three different parts.

The alpha subunit, the beta, beta subunit, and the gamma, gamma subunit.

Okay.

So what's the sequence of events?

What happens first?

Step one is the resting state.

The alpha subunit is holding onto a molecule of GDP, guanosine diphosphate.

Ehoff switch.

It's inactive, asleep, and is holding its two buddies, beta and gamma.

Then step two is activation.

A drug binds to the receptor on the outside.

And that causes the shape change.

Exactly.

The receptor twists the G protein.

This mechanical stress causes the alpha subunit to drop the GDP and pick up a GDP, guanosine diphosphate.

GDP is the energy source, the on switch.

It's the fully charged battery.

Which brings us to step three, the split.

The alpha subunit, now charged with GDP, physically separates from the beta and gamma subunits.

So they break apart.

They do.

And now you have two active units floating in the membrane.

The alpha GDP unit and the beta gamma complex, they both can slide over along the membrane and hit their targets.

And how does it stop?

We don't want the signal running forever.

Right.

Step four is culmination.

The alpha subunit has a built -in timer.

It possesses GT pace activity, meaning it slowly hydrolyzes the GDP back into GDP.

It burns its own fuel.

Exactly.

Once it's holding GDP again, it loses its affinity for the effector, grabs the beta gamma subunits, and they all group hug back together into the resting state, ready for the next signal.

It's a beautiful self -limiting cycle.

Now, the text throws an alphabet soup at us regarding the types of G proteins, GBSG, GSG, GUST.

What do these mean?

These define what the G protein actually does once it's activated.

They refer to the specific type of alpha subunit.

So GDUK stands for stimulatory.

When activated, its alpha subunit hits the enzyme, a little cyclase, and tells it to work harder.

This enzyme pumps out a second messenger called CANP, cyclic AMP.

So more CANP.

More CANP.

Now GDOF stands for inhibitory.

It hits the very same enzyme, a little cyclase, but tells it to stop.

It shuts down CMP production.

So LOLR is the gas pedal.

JOLR is the brake.

What about GQDOLR?

GQQ is a different path entirely.

It activates an enzyme called phospholipC.

This enzyme is like a pair of scissors.

It cuts membrane lipids to create two new messengers, IP3 and dia.

What do those do?

IP3 travels to the endoplasmic reticulum and opens the floodgates for calcium.

Calcium rushes into the cytoplasm.

That calcium surge is what causes smooth muscle to contract, for example.

So if you have a drug that constricts blood vessels, it's likely working through a GQ pathway.

The text also mentions a role for the GVEDA gamma, the beta gamma pair.

I thought they were just leftovers after the split.

You used to think that, but the text clarifies they have their own jobs.

The beta gamma subunits can go off and directly affect ion channels, like opening potassium channels or closing calcium channels.

So the signal actually splits into multiple outcomes.

There is a section here on biased agonism.

The text calls this a modern concept.

This feels important because it challenges the idea that a receptor is just a simple on -off switch.

It completely upends that idea.

I mean, we used to think drug gets receptor, receptor turns on, cascade happens.

So biased agonism says drug A hits the receptor, the receptor twists this way, pathway one activates.

But then drug B hits the same receptor.

The receptor twists that way, pathway two activates.

So different drugs can hit the same receptor, but trigger different downstream effects.

That's wild.

As the classic example in the text is opioids.

We want the pain relief.

That effect seems to come from the G protein pathway we just discussed.

However, the dangerous side effects like respiratory depression, stopping breathing and constipation seem to be linked to a different pathway involving a protein called beta -arrestin.

So if we could find a drug that only twists the receptor, the G protein way and ignores the beta -arrestin way.

You'd have a powerful painkiller that doesn't stop your breathing.

Yeah.

That is the holy grail of biased agonism research right now.

It shows that receptors are nuanced machines, not just light switches.

That's incredible.

Let's briefly touch on the second messengers again.

We mentioned Camp P and calcium.

Why do we call them second messengers?

Because the drug is the first messenger.

It knocked on the door, but it never entered the house.

The second messenger is the guy inside running around telling everyone what to do.

Okay.

That makes sense.

GMP, for example, usually activates protein kinases, specifically protein kinase A.

These kinases are like middle managers.

They go around adding phosphate groups to other proteins to turn them on or off.

It's a system of amplification then.

One drug molecule outside leads to thousands of Camp P molecules inside, which activate thousands of kinases.

Exactly.

A whisper becomes a shout.

The text uses the example of a paedianphrine binding to beta -2 receptors.

This raises Camp P, activates protein kinase A, which eventually phosphorylates the enzymes that break down glycogen.

The result,

a massive release of glucose energy for the muscle.

Okay.

Moving on to section five, signal transduction in other pathways.

We talked a lot about GPCRs.

How is the mechanism different for those tyrosine kinase receptors like the insulin receptor?

It's a completely different dance.

GPCRs work by recruiting a G protein.

Tyrosine kinase receptors work by recruiting each other.

What do you mean?

When insulin binds, it causes two receptor units to slide together in the membrane and pair up.

This is called dimerization.

Really like holding hands.

Basically.

And once they are paired,

they phosphorylate each other's tails.

They attach phosphate groups to the tyrosine residues on their partner.

That self -phosphorylation acts like a beacon, attracting other intracellular proteins to come get activated.

It's a recruitment drive.

And for the nuclear receptors, strictly speaking, they don't have second messengers, do they?

Not in the same way.

The receptor is the messenger.

It walks right onto the DNA.

The text splits them into type I and type II.

What's the difference there?

Type I, like sex hormones and glucocorticoids, they hang out in the cytoplasm bound to these inhibitory heat shock proteins, or HSP.

When the drug binds, the heat shock protein falls off, the receptor dimers form, and then they march into the nucleus.

And type II.

Type II, like thyroid hormone or vitamin A, are already sitting on the DNA in the nucleus, just waiting.

The ligand just has to swim in there to turn them on.

Okay, we've covered the mechanics of the machine.

Now we need to talk about how we measure the outflow.

Section six, drug efficacy and agonism.

This brings us to the most critical conceptual distinction in pharmacodynamics.

Affinity versus efficacy.

We defined affinity as stickiness.

Right.

Affinity is, can I get into the lock?

Okay.

Efficacy, or intrinsic activity, is, can I turn the key and open the door?

And you can have one without the other.

Precisely.

This gives us our fundamental definitions.

An agonist has both affinity, it binds, and the efficacy, it activates.

Simple enough.

An antagonist has affinity, so it binds, but it has zero efficacy.

It sits in the lock, but it cannot turn it.

It just blocks the hole so nothing else can get in.

That's straightforward.

But agonists get complicated.

We have full and partial.

A full agonist produces the maximal response this system is capable of.

You floor the gas pedal, a partial agonist produces a response, but it's submaximal.

Meaning?

Meaning, even if you occupy 100 % of the receptors, you might only get 50 % of the effect.

It's like a key that fits, but it's a little rusty and only turns halfway.

The text has a warning here, a crucial concept.

It says, in the presence of a full agonist, a partial agonist acts like an antagonist.

That sounds contradictory.

How can an agonist be an antagonist?

That's all about the competition for space.

Think of it like a parking lot.

If you fill every spot with a Ferrari, a full agonist, the total speed value of the lot is high.

Right.

Now imagine you fill half the spots with golf carts, or partial agonists.

The golf cart provides some speed, but it is taking up a spot that could have been used by a Ferrari.

So it's blocking the more powerful option.

It's blocking the Ferrari, so the overall average speed of the lot drops.

Yeah.

The partial agonist is antagonizing the effect of the full agonist.

So clinically, if a patient is high on a full agonist opioid like heroin, and you give them a partial agonist like buprenorphine?

You kick the heroin off the receptor because of affinity, you replace it with the weaker buprenorphine, and the patient effectively goes into withdrawal because the total signal drops.

That's a massive concept to grasp.

Now what on earth is an inverse agonist?

This one always felt like sci -fi to me.

Negative efficacy.

It requires you to change how you view receptors.

We usually assume a receptor is silent until a drug hits it.

But some receptors have what we call constitutive activity.

Meaning they're always a little bit on.

They murmur.

They are slightly active even when empty.

A neutral antagonist would just sit there and do nothing to that murmur.

But an inverse agonist binds and actively stops the murmur.

It drives activity below the baseline.

So silence isn't zero.

Silence is negative.

In this context, yes.

It stabilizes the receptor in its absolute inactive form.

Let's revisit antagonists.

We have competitive and non -competitive.

We touched on this with enzymes.

But how does it look with receptors?

It's the same logic.

Competitive antagonists bind reversibly to the agonist site.

If you add enough agonist, you can wash out the antagonist.

It is surmountable.

Okay.

Non -competitive antagonists bind irreversibly like a covalent bond or to an allosteric site.

You can add all the agonists you want.

The receptor is out of commission.

It is insurmountable.

We'll see how that changes the graphs in a minute.

But first, section seven.

Receptor regulation.

The text emphasizes that receptors aren't static.

The body fights back.

Homeostasis is the rule of life.

The cell detects the signal level.

If it's too loud, it turns the volume down.

If it's too quiet, it turns the volume up.

So if you bombard a cell with an agonist constantly.

It undergoes desensitization.

Dacufalaxis.

Right.

In the short term, the cell phosphorylates the receptor tail.

It essentially puts earplugs in.

The receptor is there, but it's disconnected from the G protein.

And if you keep doing it.

If you keep pounding it, the cell moves to down regulation.

It physically pulls the receptors inside the cell through internalization and then the laces them, destroys them.

It stops making new ones.

So you develop tolerance.

The same dose of drug does less because there are physically fewer ears to listen.

Exactly.

That's pharmacodynamic tolerance.

Now flip it.

If you block the receptors with an antagonist for a long time, like a beta blocker, the cell thinks the signal has vanished.

It gets worried.

It panics.

It starts building more receptors and putting them on the surface to try and catch any whisper of signal.

This is up regulation.

And this creates a danger when you stop the drug.

Supersensitivity.

If you abruptly stop the beta blocker, you now have a heart with 10 times the normal number of receptors.

When the body's natural adrenaline hits, it hits all of them at once.

Then you get a massive rebound effect.

Massive hypertension, arrhythmias.

That's why we always taper these drugs.

The text also ties this to a disease state.

Mycenae gravis.

That is a tragic example of receptor regulation.

It's an autoimmune disease where the body's own antibodies attack and destroy nicotinic receptors on skeletal muscles.

So the body is down regulating its own ability to move.

Essentially, yes.

We treat it by inhibiting the enzyme that breaks down acetylcholine, trying to flood the few remaining receptors to keep the muscle working.

All right.

We've arrived at the graphs.

Section 8.

Graded dose -response relationships.

Picture figure 3 .4.

The S -shaped curve.

The sigmoid curve.

On the bottom axis, the x -axis, we have the log dose of the drug.

On the side axis, the y -axis, we have the percent response.

And it goes up and flattens out.

As you increase the dose, moving right on the graph,

the response goes up slowly, then shoots up steeply, then flattens out at the top.

Let's talk about that plateau.

What does that represent?

That plateau represents Emax dollar in it.

That is your efficacy.

If you have a full agonist, the plateau is high at 100 percent.

If you have a partial agonist, the plateau is lower, maybe 50 percent.

And no matter how much more drug you give?

The curve is flat.

You can't get more response because either all receptors are full or the partial agonist just can't push harder.

And where is potency on this graph?

People confuse potency and efficacy constantly.

Potency is read off the x -axis.

It's about how far left or right the curve is.

You look for the ED50, the dose that gives 50 percent of the maximal response.

About the curve to the left.

If the curve is to the left, the ED50 is a small number.

That means the drug is potent.

You only need a pinch of it.

If the curve is to the right, the drug is less potent.

You need a bucket of it.

So you can have a drug that is very potent, a left -shifted curve, but has low efficacy, a low plateau.

Absolutely.

And you can have a drug that is weak, a right -shifted curve, but eventually reaches 100 percent efficacy if you give enough.

Now, what about spare receptors?

This concept seems weird.

How can you have spare parts?

It's a function of that amplification we talked about.

Because one receptor can trigger a massive cascade of CAMP -P, you might reach the cell's maximum physical ability to respond to contract or secrete or whatever when you've only occupied, say, 10 percent of the receptors.

So the other 90 percent are spare?

They are a safety buffer.

That means the system is very sensitive.

You don't need to find every single receptor to get a full effect.

Okay, let's visualize the antagonist on this graph.

This is classic test material.

It is.

If you add a competitive antagonist, the curve shifts to the right.

Why?

Because the antagonist is fighting for the slot.

To get the same response, you need to add more agonists to win the musical chairs game.

But,

and this is key, if you add enough, you still reach the top.

The plateau stays at 100 percent.

It is surmountable.

Right shift, but same height.

Correct.

Now, add a non -competitive antagonist.

The curve might shift right a bit, but the main feature is that the plateau gets squashed down.

Squashed down.

Because the antagonist has permanently broken some of the machines, you can never reach 100 percent response again.

No matter how much agonists you add, it is insurmountable.

Visualizing that squash effect really helps.

Finally, section nine, quantile dose response relationships.

How is this different from the graded curve we just talked about?

The graded curve looks at the magnitude of response in one person or system.

You know, how much did the blood pressure drop?

A spectrum.

Right.

The quantile curve looks at a population, and it asks a binary all or none question.

Did the event happen?

Yes or no.

Like, did the patient fall asleep?

Did the seizure stop?

Did the subject die?

Yes or no.

So the y -axis is percentage of population responding.

Right.

This is how we determine safety statistics.

We look for two key numbers from these curves.

The ED50, which is the dose that is effective for 50 percent of the population.

And the LD50.

The dose that is lethal for 50 percent of the population.

And the gap between them is the therapeutic index, or TI.

Yes.

The therapeutic index TI is the ratio.

LD50 divided by ED50.

If the lethal dose is 100 milligrams and the effective dose is 10 milligrams, the TI is 10.

Generally, a bigger number is better.

You want a wide highway between the dose that cures and the dose that kills.

But the expert in the text seems to have a problem with the TI.

They prefer the certain safety factor, CSF.

Why is that?

Well, think about the definition of TI.

It uses the median.

LD50 means that at that dose, 50 percent of your patients are dead.

That's not exactly a comforting safety benchmark.

Fair point.

Also, the slopes of the curves matter.

You could have a high TI but still have overlap where sensitive people get hurt.

The certain safety factor, CSF, is much more rigorous.

It compares the extremes.

It takes the LD1, the dose that kills just 1 percent of the population, divided by the EDDAL, the dose effective for 99 percent of the population.

So it's looking at the worst -case scenarios on both ends.

Right.

It asks the real clinical question,

is the dose I need to cure the most resistant patient

still lower than the dose that will kill the most sensitive patient?

LD1 -DAL.

And the text gives an example of this.

It does.

Phenobarbital.

Yeah.

It has a TI of 10, which sounds pretty safe, but it has a CSF of roughly 2.

That tells you that the dose needed to treat the 99th percentile is dangerously close to the dose that starts killing the first percentile.

It's a much tighter window than the TI suggests.

That is a much more sobering and, I guess, realistic way to look at safety.

We've covered a massive amount of ground here.

We really have.

From the molecular structure of the GPCR snake to the math of affinity, the signal cascades, receptor regulation, and finally how that plays out across a population.

It's a lot.

If we had to boil this down to the absolute essentials for the listener studying for a test.

Four things, I think.

One,

receptors are dynamic.

They change shape.

They regulate their numbers.

And they signal through complex cascades involving G proteins, alpha, beta, gamma.

Two,

affinity is not efficacy.

Sticking to a receptor, measured by KDR, is not the same as turning it on, measured by Emax model.

True.

Agonists and antagonists.

Understand the partial agonist parking lot concept and the surmountable nature of competitive antagonists, that right shift, versus the insurmountable noncompetitive ones.

The squash curve.

And four.

Safety.

Look beyond the therapeutic index.

Look at the overlap between efficacy and toxicity using the certain safety factor.

And I want to circle back just for a final thought on your comment about the orphan receptors.

The dark map.

It's a humbling thought, isn't it?

We have all this math, all these diagrams, and yet there are hundreds of receptors in our bodies right now that are sitting there waiting for a signal, and we have absolutely no idea what they do.

That's the beauty of pharmacology.

It looks like a Finnish science in the textbook, but it's still very much a frontier.

Somewhere in those orphan receptors is the cure for Alzheimer's or chronic pain or addiction.

We just have to find the key.

And maybe one of you listening to this deep dive will be the one to find it.

Thank you for sticking with us through the density of chapter three.

We hope this makes the text a little clearer and the board exams a little less scary.

Good luck with your studies.

A big thank you from the last minute lecture team.

We'll see you on the next deep dive.